Tools for Evolutionary and Genetic Analysis (TEGA): A new platform for the
management of molecular and environmental data
Dario Ezequiel Elias
1and Eva Carolina Rueda
1,2,3 1Universidad Nacional de Entre Ríos, Facultad de Ingeniería, Cátedra de Genética, Oro Verde, Entre Ríos,
Argentina.
2
Universidad Nacional del Litoral, Facultad de Humanidades y Ciencias, Laboratorio de Genética, Ciudad
Universitaria, Santa Fe, Argentina.
3
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
Abstract
Population genetics studies the distributions and changes in population allele frequencies in response to processes, such as mutation, natural selection, gene flow, and genetic drift. Researchers daily manage genetic, biological, and environmental data of the samples, storing them in text files or spreadsheets, which makes it difficult to maintain con-sistency and traceability. Here we present TEGA, a WEB-based stand-alone software developed for the easy analy-sis and management of population genetics data. It was designed to: 1) facilitate data management, 2) provide a way to execute the analysis procedures, and 3) supply a means to publish data, procedures, and results. TEGA is distrib-uted under the GNU AGPL v3 license. The documentation, source code, and screenshots are available at https://github.com/darioelias/TEGA. In addition, we present Rabid Fish, the first implementation of TEGA in the Ge-netics Labortory of the Faculty of Humanities and Sciences at the National University of the Litoral, where research focuses on population genetics studies applied to non-model organisms.
Keywords: Bioinformatics, biological data management, population genetics, molecular markers, Open Science.
Received: September 21, 2018; Accepted: June 25, 2019.
Introduction
As a part of evolutionary biology, population genet-ics, deals with the study of genetic differences within and between populations (Charlesworth and Charlesworth, 2017). Later, in 1987, using molecular markers (first mito-chondrial DNA and later nuclear markers), John Avise in-troduced the concept of Phylogeography as a way of explaining how historical geological, climatic, and ecologi-cal conditions influenced the current distribution of species and genetic lineages Subsequently, advances in laboratory (especially in DNA sequencing technologies) and compu-tational methods that make better use of data made phy-logeographic inferences more accurate (Avise, 1998). Phylogeography has experimented significant growth in ar-eas such as conservation, because it helps in defining evolu-tionary significant units, and in the study of prioritization for areas of high value for conservation (Moritz, 1992; Crandall et al., 1999; Frankham, 2010).
Although mitochondrial DNA has been broadly used, microsatellite markers are still valuable tools in molecular
ecological and phylogeographic studies (Jarne and Lagoda, 1996; Vignal et al., 2002; Selkoe and Toonen, 2006). Re-cent developments in next-generation sequencing approa-ches have also revolutionized the development of molecu-lar markers, allowing rapid discovery of thousands of potential microsatellite loci in the genome of model and non-model organisms (Ewers-Saucedo et al., 2016; Vartia
et al., 2016; De Barba et al., 2017; McKendrick et al.,
2017). Despite its long success in obtaining diploid robust genetic information, the analysis of a population genetics data set typically involves a variety of software packages, each of them with a different input data format (Coombs et
al., 2008). In addition, researchers daily need to manage
ge-netic, biological, and environmental data of the samples, storing them in text files or spreadsheets, which makes it difficult to maintain consistency and traceability. Here is where TEGA comes in. It is a WEB platform developed for easy data population genetics analysis and data manage-ment.
Material and Methods
TEGA was initially built with JHipster. The Back-End was developed in JAVA with Spring Boot and PostgreSQL. The procedures for the genotype analysis
Send correspondence to: Dario Ezequiel Elias. Facultad de Inge-niería de la Universidad Nacional de Entre Ríos, Cátedra de Ge-nética, Ruta prov. 11 km, 10 Oro Verde, E3101 Entre Ríos, Argen-tina. E-mail: darioezequielelias@protonmail.com.
Research Article Evolutionary Genetics
were implemented in bash using R and Python libraries. The Front-End was developed in JavaScript with AngularJS and Bootstrap.
Results
TEGA is a WEB-based stand-alone software (WEB-based platform) that aims at facilitating the daily workflow in research focused on population genetics and molecular ecology. The purpose of TEGA is to contribute to the au-tonomy of the research teams, by providing them with a means to manage, analyze, and make their data and results available.
To use TEGA, the research teams must download and install the platform on their server. TEGA has a user man-ual with instructions for its installation and use. Once in-stalled, team members will be able to import, manage, and analyze their data. When the work is finished, the data, re-sults (tables and graphics), and procedures can be accessed by other professionals through the platform.
TEGA’s objectives
Facilitating data management
TEGA has a structure based on entities to facilitate management. Every entity has screens with basic functions to: create, read, update, and delete (CRUD). It is also possi-ble to bulk import sample and genotype data (loci and al-leles). For some entities, like Samples, Projects, and Genotype Analysis, it is also possible to attach files (e.g., pictures and documents). Furthermore, given the large amount of data that can be linked to the samples, TEGA al-lows the user to create type-safe dynamic attributes and link them to different entities. In addition, it is possible to visu-alize the samples’ geographical position with OpenStreetMap (Figure 1).
TEGA has also implemented a module for manage-ment and execution of data analysis procedures. A user with the Investigator role can create the procedures and at-tach the execution and configuration files, indicate the in-put data for the procedure (e.g., sample and allele data) and the parameters for execution. These procedures can then be executed from the Genotype Analysis screen.
Providing a way to execute the analysis procedures TEGA has an entity called Genotype Analysis for the management of data related to execution of genetic analysis procedures. Initially, the user must create sample sets that contain samples grouped according to a specific criterion (e.g., sampling sites or sampling date). Then, users must create a new genotype analysis, selecting the sample sets,
loci, and the project linked to the analysis. It is then possible
to execute the analysis procedures from the platform inter-face. Once a procedure is in execution, a genotype analysis cannot be edited or deleted, and when it finishes running, the user will get access to the result files from the analysis
edition screen. In this way, TEGA links the procedure re-sults with the entry data, procedure, and parameters used, facilitating traceability of the analysis (Figure 2).
Although TEGA is designed so that users (members of the research team) can carry out their own analysis pro-cedures, in its first version we implemented common meth-ods for population genetics studies, like GENEPOP
Figure 1 - Sample screens. (A) Query screen; (B) edition and creation
screen; (C) map screen.
Figure 2 - Genotypes Analysis screens. (A) Edition and creation screen
(panel of Loci and Samples Sets); (B) query and edition screen of alleles included in the genotypes analysis; (C) screen of analysis procedures exe-cution; (D) edition and creation screen (procedures results panel).
(Rousset, 2008), STRUCTURE (Pritchard et al., 1998), and Discriminant Analysis of Principal Components (Jom-bart et al., 2010) pipelines. TEGA includes empirical data to test them (Rueda et al., 2013; Kamvar et al., 2014). The user manual has the steps to do this in a very detailed way. Supplying a means to publish data, procedures and results
TEGA has different user roles to allow private use of the data until the day of publication. Anonymous and In-vited roles are intended for people outside the research team, who have read-only access to public data. Adminis-trator and Researcher roles will only be for use by the inves-tigation team members, who have access to public and private data, and can carry out CRUD operations and exe-cute analysis procedures.
When the results of a project are published, the users of the platform (with Researcher or Administrator roles) can switch the project status, changing it to public. This ac-tion will change the status of samples, alleles, loci, and the genotype analysis related with the project, in order to be ex-plored by users with Anonymous or Invited roles. In this way, TEGA simplifies data and result publication. In addi-tion, it is also possible to change the status of the analysis procedures.
Rabid Fish
Rabid Fish is the first TEGA implementation in the Genetics Laboratory of the Faculty of Humanities and Sci-ences at the National University of the Litoral (FHUC-UNL). Its research goal is focused on population genetics studies, with emphasis in conservation questions related to non-model wetland organisms that are endangered or man-aged. The laboratory has obtained molecular markers (microsatellites) with traditional methods and NGS tech-nologies (Rueda et al., 2011a,b ; Metz et al., 2016; Ojeda et
al., 2017). The fieldwork comprises the area of the La Plata
basin, which it is the second-largest river basin of South America, including major rivers such as the Parana, Para-guay, and Uruguay systems. The research group obtained and analyzed fish samples from different migratory and commercially exploited species (Rueda et al., 2011a,b, 2013, 2017; Metz et al., 2016; Ojeda et al., 2017) resulting in a huge collection of samples donated by many collabora-tors, with different biological issues, and including more than 20 sites from five countries and four species:
Proc-hilodus lineatus, Salminus brasiliensis, Leporinus obtusi-dens, and Pseudoplatystoma corruscans.
The implementation of TEGA in the Genetics Labo-ratory of FHUC-UNL simplified data management and
al-Table 1 - Platforms for populations genetic data management.
TEGA DRIVERGENOME LOVD
Aims 1) Facilitating population genetics data management.
Assist population genetics and genetic epidemi-ology studies performed by small-medium re-search groups, by providing storage, query, and format conversion functionalities.
Web-based software for the collec-tion, display, and curation of DNA variants in locus-specific databases.
2) Providing a way to execute the analysis procedures.
3) Supplying a means to publish data, procedures and results.
Genetic data STRs SNPs, Indels, STRs and CNPs SNPs, Indels, STRs and CNPs Focused on a species No. The species is an attribute of
the samples.
Yes. Homo sapiens Yes. Homo sapiens
Tools Visualization: - Samples Map
Data format conversion tools for population ge-netics software.
Data visualization tools and reference sequence parser.
Analysis: - STRUCTURE - GENEPOP - DAPC
Extensibility The user can add or modify analy-sis procedures without needing to modify the sources or restart the platform.
New conversion tools can be added by experi-enced users.
Developers can incorporate additional tools into the source code.
Dynamic Attributes Yes No Yes
Reference This study Magalhães et al. 2012 STR = Short Tandem Repeats; SNP = Single Nucleotide Polymorphism; CNP = Copy Number Polymorphisms.
lowed to publish the data and studies results, creating a new resource for the community, a database called Rabid Fish (see Internet Resources section).
Discussion
TEGA is a WEB-based platform developed for the easy analysis and management of population genetics data. There are WEB-based platforms with purposes similar to those of TEGA, for example DRIVERGENOME (Maga-lhães et al., 2012) and LOVD (Fokkema et al., 2011) (Table 1). LOVD focuses on the collection, display, and curation of DNA variants in locus-specific databases. The aim of DRIVERGENOME is to assist population genetics and ge-netic epidemiology studies performed by small-to-medium research groups, by providing storage, query, and format conversion functionalities. Both platforms focus on model organisms for which sequenced genomes are available, par-ticularly Homo sapiens. Although these platforms offer by default tools to export genetic data in different formats and tools for the visualization of variants, they do not have tools to analyze, for example, the structure of the population.
The main advantage of TEGA is its approach to the management and execution of the analysis procedures. TEGA allows to select the samples and loci for performing a genotype analysis, from where it is possible to execute multiple procedures. In addition, the user can append new procedures to the platform without having to modify its source code, or restarting it. As the procedures can be de-veloped in any language, it is not necessary for the user to do this in the languages and frameworks used in TEGA. The intention is that the user can append the scripts he/she uses daily. In turn, the management also includes the results of the execution of the procedures, the indicated parame-ters, and the resulting files. In this way, TEGA carries out an integral management of the data, results, and proce-dures, which can then be published in the same way. We be-lieve that this feature of TEGA will facilitate the access to data and procedures, and allow the easy reproduction of studies. We believe that this is aligned with the current needs of the scientific community, as reflected in the Open Data and Open Science movements (Fecher and Friesike, 2012; Nosek et al., 2015).
Conclusions
TEGA is a WEB-based platform that aims at increas-ing the autonomy of researchers in the management, analy-sis and publication of data, procedures, and results. In the future, we plan to add other analysis tools, to integrate TEGA with other databases and improve their implementa-tion and internaimplementa-tionalizaimplementa-tion.
Acknowledgments
The authors thank Ing. G. Cagnola for help in the Ra-bid Fish implementation (Informatics Department of
FHUC-UNL) and to E. Goddio for creating TEGA and Ra-bid Fish logos. Finally, we thank L. A. Spinetta (the true Rabid Fish).
Conflict of Interest
The authors declare no conflicts of interest.
Author Contributions
DEE and ECR conceived and developed the project. DEE and ECR wrote the manuscript.
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Associate Editor: Fabrício Rodrigues dos Santos License information: This is an open-access article distributed under the terms of the Creative Commons Attribution License (type CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original article is properly cited.
